Compressive strength of concrete containing furnace blast slag; optimized machine learning-based models

Cleaner Engineering and Technology, Год журнала: 2023, Номер 13, С. 100604 - 100604

Опубликована: Фев. 13, 2023

Replacing Ordinary Portland Cement (OPC) with industrial waste like Ground Granulated Blast Furnace Slag (GGBFS) has been proven to have remarkable benefits regarding the mechanical properties of concrete and environment. The main objectives this research, as a result, are (a) develop generalized, accurate, optimized Machine Learning (ML)-based model for predicting compressive strength incorporating GGBFS (b) propose equations easier calculation containing GGBFS. To aim, various ML-based methods, namely Decision Tree (DT), Random Forest (RF), Support Vector (SVM), K-nearest Neighbors (KNN), Artificial Neural Network (ANN) were considered An extensive dataset including 625 results experimental studies was collected from international peer-reviewed publications. divided into two sub-datasets: training (85%), used train models on relationship between input output parameters, testing (15%), evaluate accuracy models. most influential ordinary cement, grade, cement ratio, water, coarse aggregate, fine age, variables proposing prediction predicted actual values compared in each model. also using common performance metrics (RMSE, MSE, MAE, MAPE, R, R2-score) Taylor diagram. Eventually, sensitivity analysis conducted at end study explore influence ratio grade strength, consequently, suggested based results.

Язык: Английский

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Estimation of transport CO2 emissions using machine learning algorithm

Transportation Research Part D Transport and Environment, Год журнала: 2024, Номер 133, С. 104276 - 104276

Опубликована: Июнь 5, 2024

Язык: Английский

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Efficient machine learning algorithm with enhanced cat swarm optimization for prediction of compressive strength of GGBS-based geopolymer concrete at elevated temperature

Construction and Building Materials, Год журнала: 2023, Номер 400, С. 132814 - 132814

Опубликована: Авг. 10, 2023

Язык: Английский

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A novel hybrid machine learning model for prediction of CO2 using socio-economic and energy attributes for climate change monitoring and mitigation policies

Ecological Informatics, Год журнала: 2023, Номер 77, С. 102253 - 102253

Опубликована: Авг. 9, 2023

Язык: Английский

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Building sustainability through a novel exploration of dynamic LCA uncertainty: Overview and state of the art

Building and Environment, Год журнала: 2024, Номер 264, С. 111922 - 111922

Опубликована: Авг. 8, 2024

Life Cycle Assessment is necessary for evaluating the environmental impacts of buildings throughout their life cycle, considering factors such as energy consumption, emissions, and resource utilization. However, Dynamic introduces a temporal dimension, acknowledging that building's performance evolves due to technological advancements, occupancy behavior, changing conditions. This paper reviews DLCA, focusing on uncertainties arising from parameter, scenario, model variability, emphasizes integration technologies like Building Information Modeling, Internet Things, machine learning enhance real-time data collection predictive analytics. An extensive review 430 papers, refined 180, reveals 55 % publications are in sciences, with significant contributions United Kingdom (27.8 %), France (24.1 China (18.1 %). Key findings include variations embodied greenhouse gas emissions materials aluminum dynamic aspects transportation impacts, which extend beyond traditional metrics operational efficiency over time. Uncertainties all LCA stages (A1 D) addressed, service life, water use, needs. Advanced methodologies, including proposed framework hybrid approach integrates process-based input-output methods, suggested comprehensiveness assessments. The monitoring analytics further improves adaptability precision models, emphasizing necessity continuous updates scenario analyses capture future conditions accurately. study paves way research aimed at mitigating major sources uncertainty, promoting more sustainable building practices, advancing field LCA.

Язык: Английский

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Potential of Advanced Technologies for Environmental Management Systems

Management Systems in Production Engineering, Год журнала: 2024, Номер 32(1), С. 33 - 44

Опубликована: Фев. 26, 2024

Abstract Environmental management systems (EMS) are essential in promoting sustainable practices and mitigating the adverse effects of human activities on environment. As technology continues to advance, there is an increasing opportunity utilize advanced technologies improve environmental systems. This article examines potential different technologies, such as artificial intelligence (AI), blockchain, big data, Internet Things (IoT), within context intends offer valuable insights researchers, practitioners, policymakers by examining uses AI, IoT The goal demonstrate how these can be leveraged enhance sustainability, boost performance, yield favourable results across sectors industries.

Язык: Английский

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AI in Structural Health Monitoring for Infrastructure Maintenance and Safety

Infrastructures, Год журнала: 2024, Номер 9(12), С. 225 - 225

Опубликована: Дек. 7, 2024

This study explores the growing influence of artificial intelligence (AI) on structural health monitoring (SHM), a critical aspect infrastructure maintenance and safety. begins with bibliometric analysis to identify current research trends, key contributing countries, emerging topics in AI-integrated SHM. We examine seven core areas where AI significantly advances SHM capabilities: (1) data acquisition sensor networks, highlighting improvements technology collection; (2) processing signal analysis, techniques enhance feature extraction noise reduction; (3) anomaly detection damage identification using machine learning (ML) deep (DL) for precise diagnostics; (4) predictive maintenance, optimize scheduling prevent failures; (5) reliability risk assessment, integrating diverse datasets real-time analysis; (6) visual inspection remote monitoring, showcasing role AI-powered drones imaging systems; (7) resilient adaptive infrastructure, enables systems respond dynamically changing conditions. review also addresses ethical considerations societal impacts SHM, such as privacy, equity, transparency. conclude by discussing future directions challenges, emphasizing potential efficiency, safety, sustainability systems.

Язык: Английский

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Preliminary Environmental Assessment of Ultra-High-Performance Concrete Mixtures

Lecture notes in civil engineering, Год журнала: 2025, Номер unknown, С. 589 - 598

Опубликована: Янв. 1, 2025

Язык: Английский

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Automated machine learning techniques for estimating the unconfined compressive strength of soil stabilization

International Journal on Interactive Design and Manufacturing (IJIDeM), Год журнала: 2025, Номер unknown

Опубликована: Янв. 15, 2025

Язык: Английский

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Machine Learning-Driven Optimization for Predicting Compressive Strength in Fly Ash Geopolymer Concrete

Cleaner Engineering and Technology, Год журнала: 2025, Номер unknown, С. 100899 - 100899

Опубликована: Янв. 1, 2025

Язык: Английский

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